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Abstract Accurate runoff forecasting is of great significance for water resource allocation flood control and disaster reduction. However, due to the inherent strong randomness of runoff sequences, this task faces significant challenges. To address this challenge, this study proposes a new SMGformer runoff forecast model. The model integrates Seasonal and Trend decomposition using Loess (STL), Informer’s Encoder layer, Bidirectional Gated Recurrent Unit (BiGRU), and Multi-head self-attention (MHSA). Firstly, in response to the nonlinear and non-stationary characteristics of the runoff sequence, the STL decomposition is used to extract the runoff sequence’s trend, period, and residual terms, and a multi-feature set based on ‘sequence-sequence’ is constructed as the input of the model, providing a foundation for subsequent models to capture the evolution of runoff. The key features of the input set are then captured using the Informer’s Encoder layer. Next, the BiGRU layer is used to learn the temporal information of these features. To further optimize the output of the BiGRU layer, the MHSA mechanism is introduced to emphasize the impact of important information. Finally, accurate runoff forecasting is achieved by transforming the output of the MHSA layer through the Fully connected layer. To verify the effectiveness of the proposed model, monthly runoff data from two hydrological stations in China are selected, and eight models are constructed to compare the performance of the proposed model. The results show that compared with the Informer model, the 1th step MAE of the SMGformer model decreases by 42.2% and 36.6%, respectively; RMSE decreases by 37.9% and 43.6% respectively; NSE increases from 0.936 to 0.975 and from 0.487 to 0.837, respectively. In addition, the KGE of the SMGformer model at the 3th step are 0.960 and 0.805, both of which can maintain above 0.8. Therefore, the model can accurately capture key information in the monthly runoff sequence and extend the effective forecast period of the model.

434 stainless steel coatings have been applied T6061 alloy using High-Velocity Oxygen Fuel (HVOF) as a deposition technique for surface performance improvement. The relationship between the coating's deposition characteristics of thickness, homogeneity, porosity, and hardness and the spraying parameters of mainly the number of sprayed-layers and spraying distance was revealed. The coating's fracture behaviors was further studied using three-point bending and digital image correlation (DIC) technology, which allows the observation of the crack progression and record the stress distribution on the coatings. Results showed that the crack progression always occur at the oxides between the different sprayed layers or surrounding semi-melted feedstock particles, the crack progression mode is mainly depending on the number and location of oxides in the coating. Then, the crack progression mainly continued through the interface between sprayed layers while the oxide content approach or exceed a critical value of 12 %. Finally, a model for the coating fracture mechanism was established to explain the interface bonding performance between the coating and the substrate.

This study focuses on the surface modification of substrates using a poly (N,N-dimethyl-N-(ethoxycarbonylmethyl)-N-[2'-(methacryloyloxy) ethyl]-ammonium chloride-co-3-(trimethoxysilyl)propylmethacrylate) P(CBMAE-co-TMSPMA) and ethyl orthosilicate (TEOS). By covalently bonding the SiOH pendant of the copolymer with hydroxyl residues on the material's surface, a stable polymer coating is formed. The properties of P(CBMAE-co-TMSPMA) coatings were evaluated using Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), scanning electron microscope (SEM), and contact angle (CA) meter. The addition of TEOS allows for the regulation of surface roughness. With the increase of TEOS content, the contact angle of underwater oil first increased and then decreased. The water contact angles were 8[degrees], the underwater superoleophobic contact angle reached 168[degrees], and the number of adherent platelets and Escherichia coli were significantly reduced to 7.5% and 5% on the surface of the coating, respectively. After undergoing simultaneous hydrolysis, the superoleophobic contact angle of P(CBMAE-co-TMSPMA)/Si[O.sub.2] coating further increased to 174[degrees]. These interesting and robust properties make them promising candidates for biomedical applications. Highlights * The underwater superoleophobic coating (contact angles of 174[degrees]). * The P(CBMAE-co-TMSPMA)/Si[O.sub.2] coating with excellent resistance to oil adhesion. * Solve the hydroxyl residue problem. * The coating resistance to E. coli and platelet adhesion. KEYWORDS anti-biofouling, Polyzwitterionic carboxylate, robust surface, underwater superoleophobic coating, 1 | INTRODUCTION Anti-fouling and anti-adhesion properties are important challenges in several disciplines, such as ship antifouling, marine engineering, (1,2) biomedical materials, (3,4) fluid transport, (5) water purification systems, food [...]

Abstract Background Diabetes mellitus is generally accompanied by dyslipidaemia, but inconsistent relationships between lipid profiles and diabetes are noted. Moreover, genetic variations in insertion/deletion (I/D) polymorphisms at angiotensin-converting enzyme gene (ACE) and T/C polymorphisms in the angiotensin type 1 receptor gene (AGTR1) are related to diabetes and lipid levels, but the associations are controversial. Thus, the current research aimed to explore the effects of ACE I/D, AGTR1 rs5182 and diabetes mellitus on serum lipid profiles in 385 Chinese participants with an average age of 75.01 years. Methods The ACE I/D variant was identified using the polymerase chain reaction (PCR) method, whereas the AGTR1 rs5182 polymorphism was identified using the PCR-based restriction fragment length polymorphism (PCR-RFLP) method and verified with DNA sequencing. Total cholesterol (TC), triglyceride (TG), apolipoprotein A (ApoA), apolipoprotein B (ApoB), high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) levels were measured using routine methods, and the lipid ratios were calculated. Results ACE I/D, but not AGTR1 rs5182, was a predictor of TG/HDL-C for the whole study population. Both ACE I/D and AGTR1 rs5182 were predictors of HDL-C and LDL-C levels in females but not in males. Moreover, in females, diabetes mellitus and ACE I/D were identified as predictors of TG and TG/HDL-C, whereas AGTR1 rs5182 and diabetes mellitus were predictors of TG/HDL-C. Moreover, diabetes mellitus and the combination of ACE I/D and AGTR1 rs5182 variations were predictors of TG and TG/HDL-C exclusively in females. Conclusions The results demonstrated the potential for gender-dependent interactions of ACE I/D, AGTR1 rs5182, and diabetes on lipid profiles. These findings may serve as an additional explanation for the inconsistent changes of blood lipids in individuals with diabetes mellitus, thereby offering a novel perspective for the clinical management of blood lipid levels in diabetic patients.

This study proposes a hybrid model based on the combination of Sand Cat Swarm Optimization (SCSO), Echo State Network (ESN), Gated Recurrent Unit (GRU), Least Squares Method (LSM), and Markov Chain (MC) to improve the accuracy of annual runoff prediction. Firstly, conduct correlation analysis on multi-factor data related to runoff to determine the input of the model. Secondly, the SCSO algorithm is used to optimize the parameters of the ESN and GRU models, and the SCSO-ESN and SCSO-GRU models are established. Next, the prediction results of these two models are coupled using LSM to obtain the preliminary prediction results of the SCSO-ESN-GRU model. Finally, the initial prediction results are corrected for errors using MC to get the final prediction results. Choose Changshui Station and Lanxi Station for experiments, and evaluate the predictive performance of the model through five evaluation indicators. The results show that the combined prediction model corrected by the MC achieved the optimal prediction performance at both experimental stations. This study emphasizes that using a combination prediction model based on Markov chain correction can significantly improve the accuracy of annual runoff prediction, providing a reliable basis for predicting annual runoff in complex watersheds. HIGHLIGHTS A new hybrid sand cat swarm optimization-echo state network-gated recurrent unit-Markov chain (SCSO-ESN-GRU-MC) model is proposed for annual runoff prediction.; A new SCSO algorithm is used to optimize the parameters of the prediction model.; Least squares method is used to couple the ESN and GRU models for annual runoff prediction.; MC is used to correct errors in annual runoff prediction results.; The proposed model achieves the best prediction results at both experimental stations.;

Abstract Ionic FETs have enormous potential for energy conversion, sensing, and ionic circuits due to their efficient regulation of the nanochannel. Here ionic FETs based on single‐crystal silicon nanopores and the rectification of the fabricated devices are studied. The electrical characterization results demonstrated that since the silicon‐based nanopores have the advantage of modulating the surface charge due to their semiconductor nature and benefitting from the effective 3D gating effect on the nanochannel, the magnitude and polarity of surface charge can be modulated by the gate voltage. The rectification effect can be adjusted by applying a certain voltage and fulfilling a transition between anion selectivity and cation selectivity when the surface charge polarity is reversed. Moreover, current–voltage characteristics of the reported ionic FET can be switched between ohmic and diode‐like regimes. The proposed ionic FETs supply a novel platform to study the ionic properties and have great potential to be applied in large‐scale ionic circuits due to their excellent performance. Finally, simulation results prove the surface charge modulated by the gate voltage determines the magnitude and direction of rectification, which is consistent with the reported experiment result.

Studies have suggested that the nucleus accumbens (NAc) is implicated in the pathophysiology of major depression; however, the regulatory strategy that targets the NAc to achieve an exclusive and outstanding anti-depression benefit has not been elucidated. Here, we identified a specific reduction of cyclic adenosine monophosphate (cAMP) in the subset of dopamine D1 receptor medium spiny neurons (D1-MSNs) in the NAc that promoted stress susceptibility, while the stimulation of cAMP production in NAc D1-MSNs efficiently rescued depression-like behaviors. Ketamine treatment enhanced cAMP both in D1-MSNs and dopamine D2 receptor medium spiny neurons (D2-MSNs) of depressed mice, however, the rapid antidepressant effect of ketamine solely depended on elevating cAMP in NAc D1-MSNs. We discovered that a higher dose of crocin markedly increased cAMP in the NAc and consistently relieved depression 24 h after oral administration, but not a lower dose. The fast onset property of crocin was verified through multicenter studies. Moreover, crocin specifically targeted at D1-MSN cAMP signaling in the NAc to relieve depression and had no effect on D2-MSN. These findings characterize a new strategy to achieve an exclusive and outstanding anti-depression benefit by elevating cAMP in D1-MSNs in the NAc, and provide a potential rapid antidepressant drug candidate, crocin.

An efficient visible-light photoelectrochemical photodetector based on a compact In-rich n-InGaN layer activated by p-Cu2O microcrystals operating as photoanode in the self-powered mode is demonstrated. The photocurrent density of 80 µA/cm2 under one-sun illumination is more than 10 times larger than that of a bare InGaN layer. Moreover, the photocurrent density, responsivity of 0.8 mA/W, specific detectivity of 4 × 109 Jones, and response times of 5–8 ms are more than five times better compared to those of our previously reported nanowire counterpart. The excellent performance is attributed to maximized photocarrier separation in the built-in electric field of the internal p–n junction for fully depleted Cu2O microcrystals with maximized height and the planar geometry, guaranteeing unhindered diffusion of the electrolyte to and from the photoanode surface.

This study examined the factors directly influencing purchase intention for vegan cosmetics by applying an extended theory of planned behavior (TPB) model with three additional constructs: environmental concern, trust, and perceived knowledge. Data were collected from 384 consumers who had purchased cosmetics at health & beauty stores in Ho Chi Minh city and Hanoi, the two biggest cities representing cosmetics consumers in Vietnam. The results revealed that all three TPB constructs had a significant positive effect on the respondents’ purchase intention for vegan cosmetics. Furthermore, environmental concern had a significant effect on all TPB constructs, while trust and perceived knowledge were positively related only to attitude and perceived behavioral control. This study provides essential insights to help firms and marketers understand the factors affecting consumers’ purchase intention and choose appropriate strategies in Vietnam’s vegan cosmetics market.